Die steel integrated continuous processing system and multi-station collaborative flow method thereof

Abstract

The application discloses a die steel integrated continuous processing system and a multi-station collaborative flow method thereof, and the system comprises an intelligent scheduling control unit, a rough machining station, a finishing machining station, a heat treatment station, a surface strengthening station, a zero loading and clamping transfer system, a process parameter collaborative optimization module and a quality whole-process tracing module. By establishing a process parameter prediction model of multi-station data fusion, seamless connection of processes and intelligent parameter collaboration are realized; by adopting heat treatment temperature field optimization based on stress distribution and self-adaptive surface strengthening strategy selection, product quality consistency is significantly improved; by utilizing a dynamic scheduling algorithm of reinforcement learning and a rolling time domain optimization, equipment utilization and production efficiency are improved. The application solves problems in traditional die steel processing, such as long process connection time, large precision cumulative error and difficult process parameter optimization, and the overall machining precision is improved by more than 30%, the production cycle is shortened by 45%, and the quality consistency is improved by 40%.

Description

Technical Field

[0001] This invention relates to the field of mold steel processing technology, specifically to an integrated continuous processing system for mold steel and its multi-station collaborative processing method. Background Technology

[0002] As a crucial basic material in the manufacturing industry, the processing quality of mold steel directly affects the performance and lifespan of molds. Traditional mold steel processing employs a segmented production model, with each process operating independently. Workpieces need to be transferred multiple times between different workshops or equipment, resulting in numerous industry pain points and severely restricting the improvement of mold steel processing accuracy and production efficiency.

[0003] The specific technical problems are as follows: 1. Long process connection time, low logistics efficiency, with the average waiting time for workpieces accounting for 40-60% of the total production cycle, resulting in severe fragmentation of production rhythm; 2. Workpieces undergo multiple clamping and positioning, which easily leads to cumulative positioning accuracy errors, significantly reducing the final machining accuracy of mold steel; 3. Frequent heating and cooling of workpieces between processes, resulting in drastic temperature fluctuations, causing instability in the metallographic structure and mechanical properties of the steel after heat treatment, and large dispersion in mold service life; 4. Independent operation of each station, resulting in uneven equipment utilization, easily leading to processing bottlenecks at some stations and idleness at others, resulting in low overall equipment utilization; 5. Isolated data for each process, lacking real-time data transmission and collaborative optimization mechanisms for process parameters between processes, making it impossible to achieve adaptive matching of the entire process; 6. No unified archiving link for production data, making quality traceability difficult, unable to accurately locate the specific process node where defects occur, and resulting in low efficiency in quality rectification.

[0004] Therefore, the industry urgently needs to develop an integrated continuous processing system and method for mold steel that can achieve multi-station collaborative flow, dynamic intelligent optimization of process parameters, full-process accuracy maintenance, and full-process quality traceability. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of the prior art and provide an integrated continuous processing system for mold steel and its multi-station collaborative flow method. This system solves the problems of inefficient connection of traditional segmented processing steps, large cumulative error in precision, inability to coordinate process parameters, poor quality stability, and difficulty in traceability. It achieves seamless connection of processes, minimizes cumulative error in precision, and intelligent coordination of process parameters, thereby significantly improving the processing efficiency and product quality of mold steel.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] An integrated continuous machining system for mold steel, comprising:

[0008] The intelligent scheduling and control unit is used to dynamically allocate tasks based on the workpiece status and the load of each workstation.

[0009] The roughing station includes a CNC milling module and a real-time force monitoring sensor;

[0010] The finishing station includes a high-precision grinding module and an online dimensional measurement device;

[0011] Heat treatment stations, including vacuum quenching furnaces and rapid tempering furnaces;

[0012] The surface strengthening station includes a laser cladding module and an ultrasonic rolling module;

[0013] Zero-clamping transfer system, including air-floating pallet and six-degree-of-freedom attitude holding device;

[0014] The process parameter collaborative optimization module establishes a process parameter prediction model based on the fusion of multi-station data.

[0015] The quality traceability module records the processing parameters, measurement data, and equipment status of each workpiece at each workstation.

[0016] The intelligent scheduling and control unit adopts a dynamic scheduling algorithm based on reinforcement learning, and the scheduling objective function is:

[0017]

[0018] in, For the i-th workpiece, Let the utilization rate of the j-th workstation be denoted as . To achieve the target average utilization rate, Let k be the waiting time for the kth transfer. , , These are the weighting coefficients.

[0019] As a further aspect of the present invention: the accuracy prediction model established by the process parameter collaborative optimization module is as follows:

[0020]

[0021] in, For the cumulative total error, Let be the processing error of the i-th process. The thermal deformation error of the i-th process is... Let be the clamping error of the i-th process. This represents the total number of processes.

[0022] As a further aspect of the present invention: the adaptive adjustment formula for the grinding parameters of the finishing station is as follows:

[0023]

[0024]

[0025] in, For grinding speed, As the reference grinding speed, This is the rough machining allowance deviation. Surface hardness, For feed rate, As the reference feed rate, For surface roughness, For residual stress, , , , This is a correction factor.

[0026] As a further aspect of the present invention: the temperature field optimization model of the heat treatment station is based on the stress distribution of the preceding processing, and the quenching temperature distribution function is:

[0027]

[0028] in, Let be the temperature at a spatial location at time t. The reference quenching temperature, For temperature adjustment range, This is a temperature correction function based on the triaxial stress components.

[0029] As a further aspect of the present invention: the attitude maintenance accuracy of the zero-clamping transfer system satisfies:

[0030]

[0031]

[0032] in, The angular deviations in the three rotational directions, The positional deviations are in the three translational directions.

[0033] A multi-station collaborative workflow method based on an integrated continuous machining system for mold steel includes the following steps:

[0034] Step 1: Workpiece information acquisition and task generation;

[0035] Step 2: Rough machining and data acquisition. Real-time force monitoring sensors collect cutting force data and calculate instantaneous cutting power.

[0036]

[0037] Step 3: Feedforward compensation of process parameters, calculate the compensation parameters for finishing:

[0038]

[0039] Step 4: Precise transfer with zero clamping;

[0040] Step 5: Adaptive finishing, real-time correction of grinding depth:

[0041]

[0042] Step 6: Stress-directed heat treatment;

[0043] Step 7: Surface strengthening strategy selection, based on surface integrity index:

[0044]

[0045] Step 8: Multi-station collaborative optimization, using Bayesian optimization algorithm to update parameters of each station:

[0046]

[0047] Step 9: Full quality traceability.

[0048] As a further aspect of the present invention: the tool wear warning in step 2 is achieved through cutting force spectrum analysis, and the warning is triggered when the characteristic frequency amplitude meets the following conditions:

[0049]

[0050] in, The current characteristic frequency amplitude, The initial characteristic frequency amplitude, This is the wear threshold coefficient, with a value ranging from 1.5 to 2.0.

[0051] As a further aspect of the present invention: the quenching temperature curve in step 6 adopts a segmented control strategy.

[0052]

[0053] in, The starting temperature, To maintain the temperature, , The heating and cooling rates.

[0054] As a further embodiment of the present invention: in step 7, laser cladding is selected when the surface integrity index SII < 0.85, and ultrasonic rolling is selected when SII ≥ 0.85.

[0055] As a further aspect of the present invention: the intelligent scheduling control unit adopts a rolling time-domain optimization strategy to predict the window length. and control window The length satisfies:

[0056]

[0057] in, For the maximum processing cycle, Sampling period

[0058] Compared with the prior art, the beneficial effects of the present invention are:

[0059] 1. Seamless process connection, completely eliminating clamping errors: The zero-clamping transfer system realizes the precise transfer of workpieces between each station, with a positioning accuracy of ≤5μm between stations, completely eliminating the cumulative positioning error caused by traditional multiple clamping, and improving the overall machining accuracy of mold steel by more than 30%.

[0060] 2. Intelligent collaboration of process parameters, resulting in excellent product consistency: A process parameter prediction model with multi-station data fusion is constructed. Through feedforward compensation and real-time closed-loop dual control, dynamic collaborative optimization of process parameters throughout the entire process is achieved, improving the consistency of mold steel product quality by 40%.

[0061] 3. Stress-guided precision heat treatment significantly improves product stability: Based on the dynamic optimization of the heat treatment temperature field according to the residual stress distribution of the previous processing, the residual stress distribution of the workpiece after heat treatment is uniform, the stress difference is reduced by 50%, effectively avoiding mold deformation and cracking problems, and extending the mold service life by 25%.

[0062] 4. Adaptive surface strengthening with extremely high process matching: Based on the surface integrity index, it automatically switches between two strengthening processes, laser cladding and ultrasonic rolling, so that the surface strengthening strategy is precisely matched with the real-time state of the workpiece, and the surface fatigue strength of the mold steel is increased by 35%.

[0063] 5. Intelligent balanced scheduling significantly improves production efficiency: By using reinforcement learning dynamic scheduling algorithms to balance the utilization rate of equipment at each workstation, bottlenecks and idleness issues in the process are eliminated, workpiece transfer waiting time is reduced by 60%, the overall production cycle is shortened by 45%, and the output per unit time is increased by 70%.

[0064] 6. Full-process digital traceability significantly improves quality control efficiency: The establishment of a digital twin archive of the entire life cycle of the workpiece reduces the time for locating quality problems from the traditional 2 hours to 15 minutes, greatly improving the efficiency of quality rectification and process iteration.

[0065] 7. Online tool warning to reduce production costs: Real-time online warning of tool wear is achieved through cutting force spectrum analysis, avoiding workpiece scrap and machining defects caused by excessive tool wear, and extending tool life by 20%. Detailed Implementation

[0066] The technical solution of the present invention will be further described in detail below with reference to specific embodiments.

[0067] An integrated continuous processing system for mold steel includes an intelligent scheduling and control unit, a roughing station, a finishing station, a heat treatment station, a surface strengthening station, a zero-clamping and transfer system, a process parameter collaborative optimization module, and a quality traceability module.

[0068] The intelligent scheduling and control unit is used to dynamically allocate tasks and schedule production cycle based on the workpiece status and real-time load of each station. The roughing station includes a CNC milling module and a real-time force monitoring sensor to remove the excess material from the mold steel blank and collect cutting force data in real time. The finishing station includes a high-precision grinding module and an online dimensional measurement device, which can adaptively adjust grinding process parameters according to the deviation of the roughing allowance. The heat treatment station includes a vacuum quenching furnace and a rapid tempering furnace, which can dynamically optimize the temperature field based on the stress distribution of the previous processing. The surface strengthening station includes a laser cladding module and an ultrasonic rolling module, which can intelligently select matching strengthening strategies according to the hardness and surface condition of the workpiece after heat treatment. The zero-clamping transfer system includes an air-floating pallet and a six-degree-of-freedom attitude holding device to realize high-precision positioning and transfer of workpieces between stations without disassembly. The process parameter collaborative optimization module establishes a process parameter prediction model based on the fusion of multi-station full-process data to accurately calculate the optimal parameter combination for each station. The quality full-process traceability module records the processing parameters, measurement data, and equipment operating status of each workpiece at each station throughout the entire process.

[0069] The intelligent scheduling and control unit adopts a dynamic scheduling algorithm based on reinforcement learning, and the scheduling objective function is:

[0070]

[0071] in, For the i-th workpiece, Let the utilization rate of the j-th workstation be denoted as . To achieve the target average utilization rate, Let k be the waiting time for the kth transfer. , , These are the weighting coefficients. The total number of workpieces. This represents the total number of workstations. This refers to the number of transfers.

[0072] The full-process accuracy prediction model established by the process parameter collaborative optimization module is as follows:

[0073]

[0074] in, For the cumulative total error, Let be the processing error of the i-th process. The thermal deformation error of the i-th process is... Let be the clamping error of the i-th process. This represents the total number of processes.

[0075] The adaptive adjustment formula for the grinding parameters of the finishing station is as follows:

[0076]

[0077]

[0078] in, For grinding speed, As the reference grinding speed, This is the rough machining allowance deviation. Surface hardness, For feed rate, As the reference feed rate, For surface roughness, For residual stress, , , , This is a correction factor.

[0079] The temperature field optimization model of the heat treatment station is constructed based on the stress distribution of the preceding processing, and the quenching temperature distribution function is:

[0080]

[0081] in, Spatial location At any moment temperature, The reference quenching temperature, For temperature adjustment range, This is a temperature correction function based on the triaxial stress components.

[0082] The attitude maintenance accuracy of the zero-clamping transfer system meets the following requirements:

[0083]

[0084]

[0085] in, The angular deviations in the three rotational directions, The positional deviations are in the three translational directions.

[0086] Secondly, the present invention provides a multi-station collaborative workflow method based on the above system, comprising the following steps:

[0087] Step 1: Workpiece Information Acquisition and Task Generation

[0088] Scan the workpiece's unique QR code to read core information such as the mold steel material grade, target size, and performance requirements. The intelligent scheduling and control unit then combines the current system equipment status, workstation load, and queue of tasks to be processed to automatically generate personalized processing tasks and allocate initial processing workstations.

[0089] Step 2: Rough Processing and Data Acquisition

[0090] The workpiece enters the roughing station, and the CNC milling module completes the removal of the blank allowance according to the preset machining path; the real-time force monitoring sensor collects three-dimensional cutting force data at a high frequency of 1kHz and calculates the instantaneous cutting power in real time.

[0091]

[0092] in, , , These are the three-dimensional cutting force components. , , The cutting speed component is the cutting speed component in the corresponding direction; when the instantaneous cutting power When the system's preset threshold is exceeded, a tool wear warning will be automatically triggered.

[0093] Furthermore, tool wear warning is achieved through cutting force spectrum analysis. A warning is triggered when the characteristic frequency amplitude meets the following conditions:

[0094]

[0095] in, The current characteristic frequency amplitude, The initial characteristic frequency amplitude, This is the wear threshold coefficient, with a value ranging from 1.5 to 2.0.

[0096] Step 3: Feedforward compensation of process parameters

[0097] After rough machining, the residual material distribution and surface stress field data of the workpiece are detected online. The process parameter co-optimization module compares the measured data with the target process standard and calculates the parameter compensation correction amount for the finishing process.

[0098]

[0099] in, For the finishing parameter correction vector, Here is the gain matrix. The measured feature vectors, The target feature vector.

[0100] Step 4: Precise Transfer with Zero-Clamping

[0101] The zero-clamping transfer system uses an air-floating pallet to adaptively adsorb and fix the workpiece. The workpiece coordinate system is kept constant throughout the process by a six-degree-of-freedom attitude holding device. There is no disassembly or secondary clamping. After being transferred to the finishing station, the workpiece positioning error is less than 5μm, completely eliminating the cumulative clamping error.

[0102] Step 5: Adaptive Finishing

[0103] The finishing station adaptively adjusts core process parameters such as grinding speed and feed rate based on feedforward compensation correction parameters; an online dimensional measurement device monitors the workpiece machining dimensions in real time, forming a closed-loop control system; when the real-time dimensional deviation exceeds 0.01mm, the grinding depth is corrected in real time using a PID algorithm.

[0104]

[0105] in, This is the amount of grinding depth correction. Due to dimensional error, , These are the proportional and integral parameters for the PID controller.

[0106] Step 6: Stress-directed heat treatment

[0107] After finishing, the system detects the residual stress distribution across the entire workpiece. The heat treatment station dynamically plans the quenching temperature curve and holding time based on the measured stress field, and adopts a segmented temperature control strategy to complete the quenching and tempering treatment, so that the stress field of the workpiece after quenching satisfies:

[0108]

[0109] in, and These represent the maximum and minimum residual stresses of the workpiece, respectively. This is the preset allowable stress difference.

[0110] The quenching temperature curve adopts a segmented control strategy, and the specific formula is as follows:

[0111]

[0112] in, The starting temperature, To maintain the temperature, This is the end time of the heating process. This is the end time for heat preservation. This is the end time of cooling. , The heating and cooling rates.

[0113] Step 7: Surface Strengthening Strategy Selection

[0114] Based on the hardness distribution of the workpiece after heat treatment and surface roughness Calculate the workpiece surface integrity index:

[0115]

[0116] in, It is a surface integrity index. For target hardness, It is surface compressive stress. , , These are the weighting coefficients.

[0117] Intelligent matching of strengthening processes based on surface integrity index: When Laser cladding is selected for surface repair and strengthening; when Ultrasonic rolling process is selected for precision surface strengthening.

[0118] Step 8: Multi-station collaborative optimization

[0119] After completing the entire processing of each workpiece, the system automatically updates the process knowledge base and uses a Bayesian optimization algorithm to iteratively update the optimal process parameters for each workstation.

[0120]

[0121] in, This represents the optimal parameter combination for the (k+1)th iteration. This is the predicted mean for quality. To predict variance, To explore and utilize trade-off coefficients.

[0122] Furthermore, the intelligent scheduling control unit employs a rolling time-domain optimization strategy to predict the window length. and control window length satisfy:

[0123]

[0124] in, For the maximum processing cycle, The sampling period.

[0125] Step 9: Full Quality Traceability

[0126] The quality traceability module records the processing timestamps, process parameters, dimensional measurement data, stress and hardness data, and equipment operating status of the workpiece at each workstation throughout the entire process, creating a unique digital twin file for each workpiece; when a quality anomaly is detected, the source process of the defect can be quickly and accurately located through reverse data tracing.

[0127] Example 1

[0128] This embodiment uses Cr12MoV cold work die steel as the processing object. The workpiece blank size is 500mm×300mm×80mm, the preset processing accuracy is ±0.008mm, the target surface roughness Ra≤0.8μm, the target hardness is HRC58-62, and the allowable residual stress difference is ≤50MPa.

[0129] After the system of this invention is powered on, it completes device self-test and parameter initialization, and the weight coefficient of the intelligent scheduling control unit is set to... , , Sampling period Maximum processing cycle The prediction window length is calculated. Control window length .

[0130] After the workpiece is scanned and its material, dimensions, and performance parameters are entered, the system automatically assigns a machining station. During the roughing stage, a real-time force monitoring sensor collects triaxial cutting forces at a frequency of 1kHz, along with the wear threshold coefficient. It can calculate cutting power and spectrum characteristics in real time to achieve accurate early warning of tool wear.

[0131] After rough machining, the system detects workpiece allowance deviation, surface stress, and roughness data, and generates finishing correction parameters through a feedforward compensation algorithm. The finishing reference grinding speed is then used. Reference feed rate The correction factors are as follows: , , , PID controller parameters , It can correct the grinding depth in real time to ensure stable dimensional accuracy.

[0132] The zero-clamping transfer system locks the workpiece posture throughout the process, with measured translational position deviation ≤0.004mm and rotational angle deviation ≤4'', and no secondary clamping errors. During the heat treatment stage, a segmented temperature control strategy is adopted: initial temperature T1=300℃, heating rate 5℃ / min, heating to 1030℃ for 40 minutes and holding for 60 minutes, followed by cooling at a rate of 3℃ / min. Combined with fine-tuning the temperature of the workpiece stress distribution area, the final residual stress difference of the workpiece is stabilized within 40MPa.

[0133] During the surface strengthening stage, the surface integrity index weights are w1=0.4, w2=0.3, and w3=0.3. In this embodiment, the SII of the workpiece after processing is 0.89, and the system automatically starts the ultrasonic rolling process. After processing, the surface roughness Ra of the workpiece is 0.62μm, and the hardness is HRC60.5, which fully meets the design requirements. If the surface performance of the workpiece is poor and SII < 0.85, the system will automatically switch to laser cladding to complete the surface repair and strengthening process.

[0134] After the entire process of workpiece processing is completed, the system updates the process parameters through Bayesian optimization and iteration to continuously optimize processing accuracy and production efficiency. At the same time, it archives the entire process processing data to form a traceable digital twin archive.

[0135] Batch testing has verified that the machining accuracy of the mold steel processed in this embodiment is 32% higher than that of traditional processes, the production cycle is shortened by 46%, the product qualification rate is increased from 88% to 99.5%, the mold service life is increased by 26%, and energy consumption is reduced by 36%, demonstrating significant technical advantages.

[0136] The preferred embodiments of the present invention have been described in detail above. However, the present invention is not limited to the above embodiments. Within the scope of knowledge possessed by those skilled in the art, various changes can be made without departing from the spirit of the present invention.

Claims

1. An integrated continuous processing system for mold steel, characterized in that, include: The intelligent scheduling and control unit is used to dynamically allocate tasks based on the workpiece status and the load of each workstation. The roughing station includes a CNC milling module and a real-time force monitoring sensor; The finishing station includes a high-precision grinding module and an online dimensional measurement device; Heat treatment stations, including vacuum quenching furnaces and rapid tempering furnaces; The surface strengthening station includes a laser cladding module and an ultrasonic rolling module; Zero-clamping transfer system, including air-floating pallet and six-degree-of-freedom attitude holding device; The process parameter collaborative optimization module establishes a process parameter prediction model based on the fusion of multi-station data. The quality traceability module records the processing parameters, measurement data, and equipment status of each workpiece at each workstation. The intelligent scheduling and control unit adopts a dynamic scheduling algorithm based on reinforcement learning, and the scheduling objective function is: in, The total processing cycle for the i-th workpiece is... Let the utilization rate of the j-th workstation be denoted as . To achieve the target average utilization rate, Let k be the waiting time for the kth transfer. , , These are the weighting coefficients.

2. The integrated continuous machining system for mold steel according to claim 1, characterized in that, The accuracy prediction model established by the process parameter collaborative optimization module is as follows: in, For the cumulative total error, Let be the processing error of the i-th process. The thermal deformation error of the i-th process is... Let be the clamping error of the i-th process. This represents the total number of processes.

3. The integrated continuous machining system for mold steel according to claim 1, characterized in that, The adaptive adjustment formula for the grinding parameters of the finishing station is as follows: , in, For grinding speed, As the reference grinding speed, This is the rough machining allowance deviation. Surface hardness, For feed rate, As the reference feed rate, For surface roughness, For residual stress, , , , This is a correction factor.

4. The integrated continuous machining system for mold steel according to claim 1, characterized in that, The temperature field optimization model of the heat treatment station is based on the stress distribution of the preceding processing, and the quenching temperature distribution function is: in, Let be the temperature at a spatial location at time t. The reference quenching temperature, For temperature adjustment range, This is a temperature correction function based on the triaxial stress components.

5. The integrated continuous machining system for mold steel according to claim 1, characterized in that, The attitude maintenance accuracy of the zero-clamping transfer system meets the following requirements: in, The angular deviations in the three rotational directions, The positional deviations are in the three translational directions.

6. A multi-station collaborative workflow method based on the system described in any one of claims 1-5, characterized in that, Includes the following steps: Step 1: Workpiece information acquisition and task generation; Step 2: Rough machining and data acquisition. Real-time force monitoring sensors collect cutting force data and calculate instantaneous cutting power. Step 3: Process parameter feedforward compensation, calculate the compensation parameters for finishing: ； Step 4: Zero-clamping precision transfer; Step 5: Adaptive finishing, real-time correction of grinding depth: Step 6: Stress-directed heat treatment; Step 7: Surface strengthening strategy selection, based on surface integrity index: Step 8: Multi-station collaborative optimization, using Bayesian optimization algorithm to update parameters of each station: Step 9: Full quality traceability.

7. The multi-station collaborative workflow method according to claim 6, characterized in that, The tool wear warning in step 2 is achieved through cutting force spectrum analysis. The warning is triggered when the characteristic frequency amplitude meets the following conditions: in, The current characteristic frequency amplitude, The initial characteristic frequency amplitude, This is the wear threshold coefficient, with a value ranging from 1.5 to 2.

0.

8. The multi-station collaborative workflow method according to claim 6, characterized in that, The quenching temperature curve in step 6 adopts a segmented control strategy: in, The starting temperature, To maintain the temperature, , The heating and cooling rates.

9. The multi-station collaborative workflow method according to claim 6, characterized in that, In step 7, laser cladding is selected when the surface integrity index (SII) is < 0.85, and ultrasonic rolling is selected when SII is ≥ 0.

85.

10. The multi-station collaborative workflow method according to claim 6, characterized in that, The intelligent scheduling and control unit employs a rolling time-domain optimization strategy to predict the window length. and control window The length satisfies: in, For the maximum processing cycle, The sampling period.

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